Stepwise modelling of biochemical pathways based on qualitative model learning

Modelling of biochemical pathways in a computational way has received considerable attention over the last decade from biochemistry, computing sciences, and mathematics. In this paper we present an approach to evolutionarily stepwise constructing models of biochemical pathways by a qualitative model learning methodology. Given a set of reactants involved in a target biochemical pathway, atomic components can be generated and preserved in a components library for further model composition. These synthetic components are then reused to compose models which are qualitatively evaluated by referring to experimental qualitative states of the given reactants. Simulation results show that our stepwise evolutionary qualitative model learning approach can learn the relationships among reactants in biochemical pathway, by exploring topology space of alternative models. In addition, synthetic biochemical complex can be obtained as hidden reactants in composed models. The inferred hidden reactants and topologies of the synthetic models can be further investigated by biologists in experimental environment for understanding biological principles.